ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Apr 2026
Jan 2026
Latest Journal Issues
Nuclear Science and Engineering
June 2026
Nuclear Technology
March 2026
Fusion Science and Technology
May 2026
Latest News
DOE selects first companies for nuclear launch pad
The Department of Energy’s Office of Nuclear Energy and the National Reactor Innovation Center have announced their first selections for the Nuclear Energy Launch Pad: three companies developing microreactors and one developing fuel supply.
The four companies—Deployable Energy, General Matter, NuCube Energy, and Radiant Industries—were selected from the initial pool of Reactor Pilot Program and Fuel Line Pilot Program applicants, the two precursor programs to the launch pad.
J. G. Stevens, K. S. Smith, K. R. Rempe, T. J. Downar
Nuclear Science and Engineering | Volume 121 | Number 1 | September 1995 | Pages 67-88
Technical Paper | doi.org/10.13182/NSE121-67
Articles are hosted by Taylor and Francis Online.
Simulated-annealing optimization of reactor core loading patterns is implemented with support for design heuristics during candidate pattern generation. The SIMAN optimization module uses the advanced nodal method of SIMULATE-3 and the full cross-section detail of CASMO-3 to evaluate accurately the neutronic performance of each candidate, resulting in high-quality patterns. The use of heuristics within simulated annealing is explored. Heuristics improve the consistency of optimization results for both fast- and slow-annealing runs with no penalty from the exclusion of unusual candidates. Thus, the heuristic application of designer judgment during automated pattern generation is shown to be effective. The capability of the SIMAN module to find and evaluate families of loading patterns that satisfy design constraints and have good objective performance within practical run times is demonstrated. The use of automatic evaluations of successive cycles to explore multicycle effects of design decisions is discussed.